Cargando…

Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review

Studies have shown that the use of artificial intelligence can reduce errors in medical image assessment. The diagnosis of breast cancer is an essential task; however, diagnosis can include ‘detection’ and ‘interpretation’ errors. Studies to reduce these errors have shown the feasibility of using co...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wong, Dennis Jay, Gandomkar, Ziba, Wu, Wan‐Jing, Zhang, Guijing, Gao, Wushuang, He, Xiaoying, Wang, Yunuo, Reed, Warren
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	John Wiley and Sons Inc. 2020
Materias:	Review Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7276180/ https://www.ncbi.nlm.nih.gov/pubmed/32134206 http://dx.doi.org/10.1002/jmrs.385

Ejemplares similares

Artificial Intelligence in medical imaging practice: looking to the future
por: Lewis, Sarah J, et al.
Publicado: (2019)

Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review
por: Gastounioti, Aimilia, et al.
Publicado: (2022)

Automated Lung Cancer Detection Using Artificial Intelligence (AI) Deep Convolutional Neural Networks: A Narrative Literature Review
por: Sathyakumar, Kaviya, et al.
Publicado: (2020)

Using Occlusion-Based Saliency Maps to Explain an Artificial Intelligence Tool in Lung Cancer Screening: Agreement Between Radiologists, Labels, and Visual Prompts
por: Gandomkar, Ziba, et al.
Publicado: (2022)

Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy
por: Choi, Joonmyeong, et al.
Publicado: (2020)

Mammographic screening and mammographic patterns
por: Day, Nick, et al.
Publicado: (2000)

A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis
por: Zou, Lian, et al.
Publicado: (2019)

Understanding mammographic breast density profile in China: A Sino‐Australian comparative study of breast density using real‐world data from cancer screening programs
por: Li, Tong, et al.
Publicado: (2022)

Characteristics of Mammographic Breast Density and Associated Factors for Chinese Women: Results from an Automated Measurement
por: Li, Tong, et al.
Publicado: (2019)

Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns
por: Liimatainen, Kaisa, et al.
Publicado: (2021)

Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks
por: Wang, Dawei, et al.
Publicado: (2022)

Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility Study
por: Tao, Xuetong, et al.
Publicado: (2023)

Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density
por: Mullooly, Maeve, et al.
Publicado: (2019)

Adapting the pre-trained convolutional neural networks to improve the anomaly detection and classification in mammographic images
por: Saber, Abeer, et al.
Publicado: (2023)

Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology
por: Ramakrishna, Ramanaesh Rao, et al.
Publicado: (2020)

Influence of the Depth of the Convolutional Neural Networks on an Artificial Intelligence Model for Diagnosis of Orthognathic Surgery
por: Kim, Ye-Hyun, et al.
Publicado: (2021)

Deep Learning in Urological Images Using Convolutional Neural Networks: An Artificial Intelligence Study
por: Serel, Ahmet, et al.
Publicado: (2022)

Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
por: Mahmood, Tariq, et al.
Publicado: (2022)

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network
por: Rahman, Hameedur, et al.
Publicado: (2023)

Fully convolutional network for automated detection and diagnosis of mammographic masses
por: Kulkarni, Sujata, et al.
Publicado: (2023)

Identification and diagnosis of mammographic malignant architectural distortion using a deep learning based mask regional convolutional neural network
por: Liu, Yuanyuan, et al.
Publicado: (2023)

An artificial intelligent diagnostic system on mobile Android terminals for cholelithiasis by lightweight convolutional neural network
por: Pang, Shanchen, et al.
Publicado: (2019)

Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence
por: Nafisah, Saad I., et al.
Publicado: (2022)

Artificial intelligence convolutional neural networks map giant kelp forests from satellite imagery
por: Marquez, L., et al.
Publicado: (2022)

BI-RADS-Based Classification of Mammographic Soft Tissue Opacities Using a Deep Convolutional Neural Network
por: Sabani, Albin, et al.
Publicado: (2022)

Artificial Intelligence in Dentistry—Narrative Review
por: Ossowska, Agata, et al.
Publicado: (2022)

External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules
por: Baldwin, David R, et al.
Publicado: (2020)

Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network
por: Islam, Md. Mohaimenul, et al.
Publicado: (2021)

Artificial intelligence and capsule endoscopy: automatic detection of vascular lesions using a convolutional neural network
por: Ribeiro, Tiago, et al.
Publicado: (2021)

Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals
por: Kourounis, Georgios, et al.
Publicado: (2023)

Artificial intelligence and anesthesia: A narrative review
por: Singh, Madhavi, et al.
Publicado: (2022)

Artificial intelligence and anesthesia: a narrative review
por: Bellini, Valentina, et al.
Publicado: (2022)

Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments
por: Gan, Kaifeng, et al.
Publicado: (2019)

Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark
por: Wang, Xu, et al.
Publicado: (2020)

Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints
por: Hu, Xiaobin, et al.
Publicado: (2021)

Artificial intelligence for diagnosing neoplasia on digital cholangioscopy: development and multicenter validation of a convolutional neural network model
por: Robles-Medranda, Carlos, et al.
Publicado: (2023)

A convolutional deep learning model for improving mammographic breast-microcalcification diagnosis
por: Kang, Daesung, et al.
Publicado: (2021)

Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study
por: Rodriguez-Ruiz, Alejandro, et al.
Publicado: (2019)

Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy – Artificial intelligence versus clinician for screening
por: Shah, Payal, et al.
Publicado: (2020)

Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network
por: Liu, Shang-Long, et al.
Publicado: (2019)

Cannot write session to /tmp/vufind_sessions/sess_tdquqfrjoj7hsneautuvf4g2p5